





'''simple docstring'''






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   """Hello world! cécé herlolip"""






def   __A    (				a_    :	str   ,a_    :	str   ,a_    :	bool						):
		lowerCAmelCase  :     List[str]       						= FairseqRobertaModel.from_pretrained(a_						)
		roberta.eval()  # disable dropout
		lowerCAmelCase  :     List[str]       						= roberta.model.encoder.sentence_encoder
		lowerCAmelCase  :     Tuple       						= XLMRobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=roberta.cfg.model.encoder_embed_dim   ,num_hidden_layers=roberta.cfg.model.encoder_layers   ,num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,max_position_embeddings=5_1_4   ,type_vocab_size=1   ,layer_norm_eps=1e-5   ,)
		if classification_head:
				lowerCAmelCase  :     Union[str, Any]       						= roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

		print("Our RoBERTa config:"   ,a_						)

		lowerCAmelCase  :     List[str]       						= XLMRobertaXLForSequenceClassification(a_						) if classification_head else XLMRobertaXLForMaskedLM(a_						)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		lowerCAmelCase  :     List[str]       						= roberta_sent_encoder.embed_tokens.weight
		lowerCAmelCase  :     Tuple       						= roberta_sent_encoder.embed_positions.weight
		lowerCAmelCase  :     str       						= torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

		lowerCAmelCase  :     Tuple       						= roberta_sent_encoder.layer_norm.weight
		lowerCAmelCase  :     int       						= roberta_sent_encoder.layer_norm.bias

		for i in range(config.num_hidden_layers						):
				# Encoder: start of layer
				lowerCAmelCase  :     BertLayer       						= model.roberta.encoder.layer[i]
				lowerCAmelCase  :     TransformerSentenceEncoderLayer       						= roberta_sent_encoder.layers[i]

				lowerCAmelCase  :     RobertaAttention       						= layer.attention
				lowerCAmelCase  :     int       						= roberta_layer.self_attn_layer_norm.weight
				lowerCAmelCase  :     Optional[Any]       						= roberta_layer.self_attn_layer_norm.bias

				# self attention
				lowerCAmelCase  :     BertSelfAttention       						= layer.attention.self
				assert (
				    roberta_layer.self_attn.k_proj.weight.data.shape
				    == roberta_layer.self_attn.q_proj.weight.data.shape
				    == roberta_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)						)
				)

				lowerCAmelCase  :     Dict       						= roberta_layer.self_attn.q_proj.weight
				lowerCAmelCase  :     Dict       						= roberta_layer.self_attn.q_proj.bias
				lowerCAmelCase  :     Optional[Any]       						= roberta_layer.self_attn.k_proj.weight
				lowerCAmelCase  :     Tuple       						= roberta_layer.self_attn.k_proj.bias
				lowerCAmelCase  :     Union[str, Any]       						= roberta_layer.self_attn.v_proj.weight
				lowerCAmelCase  :     Dict       						= roberta_layer.self_attn.v_proj.bias

				# self-attention output
				lowerCAmelCase  :     BertSelfOutput       						= layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				lowerCAmelCase  :     Any       						= roberta_layer.self_attn.out_proj.weight
				lowerCAmelCase  :     List[Any]       						= roberta_layer.self_attn.out_proj.bias

				# this one is final layer norm
				lowerCAmelCase  :     Any       						= roberta_layer.final_layer_norm.weight
				lowerCAmelCase  :     Any       						= roberta_layer.final_layer_norm.bias

				# intermediate
				lowerCAmelCase  :     BertIntermediate       						= layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				lowerCAmelCase  :     Dict       						= roberta_layer.fca.weight
				lowerCAmelCase  :     Union[str, Any]       						= roberta_layer.fca.bias

				# output
				lowerCAmelCase  :     BertOutput       						= layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				lowerCAmelCase  :     Optional[Any]       						= roberta_layer.fca.weight
				lowerCAmelCase  :     Tuple       						= roberta_layer.fca.bias
				# end of layer

		if classification_head:
				lowerCAmelCase  :     List[str]       						= roberta.model.classification_heads["mnli"].dense.weight
				lowerCAmelCase  :     List[str]       						= roberta.model.classification_heads["mnli"].dense.bias
				lowerCAmelCase  :     Optional[Any]       						= roberta.model.classification_heads["mnli"].out_proj.weight
				lowerCAmelCase  :     Union[str, Any]       						= roberta.model.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				lowerCAmelCase  :     Dict       						= roberta.model.encoder.lm_head.dense.weight
				lowerCAmelCase  :     List[str]       						= roberta.model.encoder.lm_head.dense.bias
				lowerCAmelCase  :     Dict       						= roberta.model.encoder.lm_head.layer_norm.weight
				lowerCAmelCase  :     Dict       						= roberta.model.encoder.lm_head.layer_norm.bias
				lowerCAmelCase  :     int       						= roberta.model.encoder.lm_head.weight
				lowerCAmelCase  :     Dict       						= roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		lowerCAmelCase  :     torch.Tensor       						= roberta.encode(a_						).unsqueeze(0						)  # batch of size 1

		lowerCAmelCase  :     Union[str, Any]       						= model(a_						)[0]
		if classification_head:
				lowerCAmelCase  :     int       						= roberta.model.classification_heads["mnli"](roberta.extract_features(a_						)						)
		else:
				lowerCAmelCase  :     Optional[Any]       						= roberta.model(a_						)[0]
		print(our_output.shape   ,their_output.shape						)
		lowerCAmelCase  :     Optional[int]       						= torch.max(torch.abs(our_output - their_output						)						).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
		lowerCAmelCase  :     Union[str, Any]       						= torch.allclose(a_   ,a_   ,atol=1e-3						)
		print("Do both models output the same tensors?"   ,"🔥" if success else "💩"						)
		if not success:
				raise Exception("Something went wRoNg"						)

		pathlib.Path(a_						).mkdir(parents=a_   ,exist_ok=a_						)
		print(f'''Saving model to {pytorch_dump_folder_path}'''						)
		model.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



